Data Visualization in R with ggplot2

Josh Quan

Introduction

“The simple graph has brought more information to the data analyst’s mind than any other device.”
— John Tukey

Data visualization is the creation and study of the visual representation of data.
Many tools for visualizing data (R is one of them)
Many approaches/systems within R for making data visualizations, ggplot2 is one of them

ggplot2 \(\in\) tidyverse

ggplot2: tidyverse’s data visualization package
gg in “ggplot2” stands for Grammar of Graphics
Inspired by the book Grammar of Graphics by Leland Wilkinson
A grammar of graphics is a tool that enables concise description of components of a graphic

ggplot2 \(\in\) tidyverse

Dataset

Stanford Open Policing Project

Police Stop Data
- state, driver race, stop rate, marijuana legalization status

stops <- readr::read_csv("./data/stops.csv")

str(stops)

## spc_tbl_ [114 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ state              : chr [1:114] "CO" "CO" "CO" "CO" ...
##  $ driver_race        : chr [1:114] "white" "white" "white" "white" ...
##  $ pre_legalization   : logi [1:114] TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ quarter            : Date[1:114], format: "2011-02-15" "2011-05-15" ...
##  $ search_rate        : num [1:114] 0.00454 0.00402 0.00356 0.00373 0.00449 ...
##  $ legalization_status: chr [1:114] "pre" "pre" "pre" "pre" ...
##  $ search_rate_100    : num [1:114] 0.454 0.402 0.356 0.373 0.449 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   state = col_character(),
##   ..   driver_race = col_character(),
##   ..   pre_legalization = col_logical(),
##   ..   quarter = col_date(format = ""),
##   ..   search_rate = col_double(),
##   ..   legalization_status = col_character(),
##   ..   search_rate_100 = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

head(stops)

## # A tibble: 6 × 7
##   state driver_race pre_legalization quarter    search_rate legalizati…¹ searc…²
##   <chr> <chr>       <lgl>            <date>           <dbl> <chr>          <dbl>
## 1 CO    white       TRUE             2011-02-15     0.00454 pre            0.454
## 2 CO    white       TRUE             2011-05-15     0.00402 pre            0.402
## 3 CO    white       TRUE             2011-08-15     0.00356 pre            0.356
## 4 CO    white       TRUE             2011-11-15     0.00373 pre            0.373
## 5 CO    white       TRUE             2012-02-15     0.00449 pre            0.449
## 6 CO    white       TRUE             2012-05-15     0.00450 pre            0.450
## # … with abbreviated variable names ¹legalization_status, ²search_rate_100

Basic ggplot2 syntax

DATA
MAPPING
GEOM

Step-by-step

library(ggplot2)
ggplot(data = stops)

ggplot(data = stops, mapping = aes(x = quarter, y = search_rate_100))

ggplot(data = stops, mapping = aes(x = quarter, y = search_rate_100)) +
  geom_point()

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_point()

Police Searches Drop Dramatically in States that Legalized Marijuana

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth()

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(method = "loess")

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(method = "loess", se = FALSE)

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(method = "loess", se = FALSE) +
  scale_color_viridis_d()

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(method = "loess", se = FALSE) +
  scale_color_viridis_d() +
  theme_minimal()

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(method = "loess", se = FALSE) +
  scale_color_viridis_d() +
  theme_minimal() +
  labs(x = "Year", y = "Search Rate", color = "Driver Race",
       title = "Washington Highway Patrol Searches", subtitle = "Searches Per Hundred stops")

Mapping variables

Size data points by a numerical variable

ggplot(data = stops, aes(x = quarter, y = search_rate_100, size = search_rate_100)) +
  geom_point()

Set alpha value

ggplot(data = stops, aes(x = quarter, y = search_rate_100, size = search_rate_100)) +
  geom_point(alpha = 0.5)

Your turn!

Exercise: Using information from https://ggplot2.tidyverse.org/articles/ggplot2-specs.html add color, size, alpha, and shape aesthetics to your graph. Experiment. Do different things happen when you map aesthetics to discrete and continuous variables? What happens when you use more than one aesthetic?

stops %>% ggplot(aes(x = quarter , y = search_rate_100, color = driver_race)) + 
  geom_point() + 
  theme_minimal(base_size = 12)

Mappings can be at the `geom` level

ggplot(data = stops) +
  geom_point(mapping = aes(x = quarter, y = search_rate_100))

Different mappings for different `geoms`

ggplot(data = stops, mapping = aes(x = quarter, y = search_rate_100)) +
  geom_point() +
  geom_smooth(aes(color = driver_race), method = "loess", se = FALSE)

Set vs. map

To map an aesthetic to a variable, place it inside aes(). Think of this as a global rule

ggplot(data = stops, 
  mapping = aes(x = quarter, 
                y = search_rate_100,
            color = driver_race)) +
  geom_point()

To set an aesthetic to a value, place it outside aes(). Think of this as a local setting

ggplot(data = stops, 
  mapping = aes(x = quarter, 
                y = search_rate_100)) +
  geom_point(color = "red")

Can specify HTML color codes

ggplot(data = stops, 
  mapping = aes(x = quarter, 
                y = search_rate_100)) + 
  geom_point(color = "#63B3E8")

Data can be passed in

stops %>%
  ggplot(aes(x = quarter, y = search_rate_100)) +
    geom_point()

Assign ggplot() to objects for layering

p <- ggplot(stops, aes(x = quarter, y = search_rate_100)) +
  geom_point()

p + geom_smooth()

Common early pitfalls

Mappings that aren’t

ggplot(data = stops) +
  geom_point(aes(x = quarter, y = search_rate_100, color = "blue"))

Mappings that aren’t

ggplot(data = stops) +
  geom_point(aes(x = quarter, y = search_rate_100), color = "blue")

Your turn!

Exercise: What is wrong with the following?

stops %>%
  ggplot(aes(x = quarter, y = search_rate_100, color = legalization_status)) %>%
    geom_point()

+ and %>%

What is wrong with the following?

stops %>%
  ggplot(aes(x = quarter, y = search_rate_100, color = legalization_status)) %>%
    geom_point()

## Error in `geom_point()`:
## ! `mapping` must be created by `aes()`.
## ℹ Did you use `%>%` or `|>` instead of `+`?

Basic plot

ggplot(data = stops, aes(x = quarter, y = search_rate_100)) +
  geom_point()

Two layers

ggplot(data = stops, aes(x = quarter, y = search_rate_100)) +
  geom_point()  +
  geom_line()

The power of groups

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() + 
  geom_line()

Now we’ve got it

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_smooth(span = 0.2, se = FALSE)

Control data by layer

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = filter(stops, search_rate_100 < .2),
             size = 5, color = "gray") +
  geom_point()

Your turn!

Exercise: Work with your neighbor to sketch what the following plots will look like. No cheating! Do not run the code, just think through the code for the time being.

pre_legalization_high <- stops %>%
  filter((quarter < "2013-01-01" & search_rate_100 > 1.0))

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point() +
  geom_text(data = pre_legalization_high, aes(y = search_rate_100 + .05, label = search_rate_100), 
            size = 2, color = "black")

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point()

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  geom_point(data = pre_legalization_high, size = 5, color = "gray")

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point()

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point() +
  geom_text(data = pre_legalization_high, aes(y = search_rate_100, label = search_rate_100), 
            size = 2, color = "black")

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point() +
  geom_text(data = pre_legalization_high, aes(y = search_rate_100 + .05, label = search_rate_100), 
            size = 2, color = "black")

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point() + 
  geom_text_repel(data = pre_legalization_high, 
                  aes(x = quarter, y = search_rate_100, 
                      label = as.character(quarter)), 
                  size = 3, color = "black")

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point(data = pre_legalization_high, size = 5, color = "gray") +
  geom_point() + 
  geom_label_repel(data = pre_legalization_high, 
                  aes(x = quarter, y = search_rate_100, 
                      label = as.character(quarter)), 
                  size = 3, color = "black")

Specifying colors

ggplot(stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  scale_color_manual(values = c("#FF6EB4", "#00BFFF", "#008B8B")) + 
  geom_smooth(se = FALSE)

ggplot(data = stops, aes(x = quarter, y = search_rate_100)) +
  geom_smooth() +
  facet_wrap( ~ driver_race)

ggplot(data = stops, aes(x = quarter, y = search_rate_100)) +
  geom_line() +
  facet_grid(state ~ driver_race)

ggplot(data = stops, aes(x = quarter, y = search_rate_100)) +
  geom_line() +
  facet_grid(driver_race ~ state)

Scales and legends

Scale transformation

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  scale_y_reverse()

Scale transformation

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  scale_y_sqrt()

Scale details

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  scale_y_continuous(breaks = c(0, 0.25, 0.5, .75, 1.0))

Themes

Overall themes

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  theme_bw()

Overall themes

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  theme_dark()

Customizing theme elements

ggplot(data = stops, aes(x = quarter, y = search_rate_100, color = driver_race)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90))

Combining several plots to a grid

wa_stops <- stops %>% filter(state == "WA") %>% 
  ggplot(aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(se = FALSE) + 
  labs(title = "Washington")

co_stops <- stops %>% filter(state == "CO") %>% 
  ggplot(aes(x = quarter, y = search_rate_100, color = driver_race)) + 
  geom_smooth(se = FALSE) + 
  labs(title = "Colorado") + 
  theme(legend.position = "none")

Combining several plots to a grid

wa_stops + co_stops

(wa_stops / co_stops)

Interactivity

plotly::ggplotly(wa_stops)

Your turn!

Final Exercise:

Recreate this chart

Starter code:

stops %>% filter(state == "WA") %>% 
  ggplot(aes(quarter, search_rate_100, color = driver_race)) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE)

‘?labs’ layer controls title, subtitle, caption, etc.
‘?scale_color_manual’ layer allows you to specify your own colors to the levels
‘?geom_vline’ layer draws a vertical line across the plot. (hint: the x-axis is a date data type)
‘?theme’ controls the non-data elements of the plot like size of text, angle of axis ticks, etc.
‘?annotate’ creates a text annotation layer. Same trick with coordinates as geom_vline
Experiment with themes

Themes Vignette

To really master themes:

ggplot2.tidyverse.org/articles/extending-ggplot2.html#creating-your-own-theme

Recap

Takeaways

map variables to aethestics
add “geoms” for visual representation layers
scales can be independently managed
legends are automatically created but can be managed
statistics are sometimes calculated by geoms so understand how geoms work

ggplot2 template

Make any plot by filling in the parameters of this template

knitr::include_graphics("./img/ggplot2-template.png")

Learn more

Books:
- R for Data Science by Grolemund and Wickham
- R Graphics Cookbook by Chang
- Data Visualization: A Practical Introduction by Healy
ggplot2.tidyverse.org
ggplot2 Cheat sheet

Data Visualization in R with ggplot2

Introduction

ggplot2 \(\in\) tidyverse

ggplot2 \(\in\) tidyverse

ggplot2 \(\in\) tidyverse

ggplot2 \(\in\) tidyverse

Dataset

Basic ggplot2 syntax

Step-by-step

Mapping variables

Size data points by a numerical variable

Set alpha value

Your turn!

Mappings can be at the geom level

Different mappings for different geoms

Set vs. map

Data can be passed in

Assign ggplot() to objects for layering

Common early pitfalls

Mappings that aren’t

Mappings that aren’t

Your turn!

+ and %>%

Basic plot

Two layers

The power of groups

Now we’ve got it

Control data by layer

Your turn!

Specifying colors

Splitting over facets

facet_grid

facet_grid

Scales and legends

Scale transformation

Scale transformation

Scale details

Themes

Overall themes

Overall themes

Customizing theme elements

Combining several plots to a grid

Combining several plots to a grid

Interactivity

Your turn!

Themes Vignette

Recap

Takeaways

ggplot2 template

Learn more

Mappings can be at the `geom` level

Different mappings for different `geoms`